2 • We will investigate the results and discuss them later. • 2 goodies will be raffled off among all participats. • Perfect if you are bored, the talk is not as expected – and to be allowed using your smartphone during the presentation. How are you using AI in your daily work? Participate & win Participate & win

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Azure Bootcamp Switzerland 05/16/2024 Implementing AI: successes & lessons from a software agency 4

Tobias Kluge «Mr. AI», Nexplore AG We are hiring!

Agenda 6 1. State of AI 2. Implementation 3. Tooling 4. Results of you 5. Raffle

7 State of AI

8 Neuronal network – the brain of AI systems 0.722 0.124 0.542 0.218 0.322 0.876 0.473 0.146 AI model = billions of numbers = probabilities AI does not really understand what it does or knows (and what not)

9 Training of AI systems AI [0.421, 0.141, …] [0.811, 0.321, …] Take care of YOUR data!

10 • https://ourworldindata.org/brief-history-of-ai Development of AI AGI

11 OpenAI GPT4o – AGI 0.9 https://openai.com/index/hello-gpt-4o/

12 Levels of «AI» Products with build-in AI Configuration of ready to use AI-tools Building ai applications with base models, fine- tuning & training

13 What is AI (in this session today)? LLM Data

14 Implementation of AI inside of your organization Organisation readiness

15 It’s so easy… Explore & define use cases Identify minimal solution for use cases Test in pilot Ship to user base

16 • First things first: • Provide base rules (no PII and customer data to public cloud) • Gover your data – best with classification & labeling • Bottom up • Train your people about the base rules & let them experiment • Start small – experiments, «lean» approach • With management support, of course • Have a «AI user group» and «C-AI-O» that supports decisions in the organisation • Communicate well! • Invest! time, people, ressources and money Explore & define use cases

17 • Check the product – LLM model, data base, update frequency, internet-connection, prototype vs ready for production • Check the privacy: monitoring, training, reselling, …? • Check the pricing: usage-based, per user, enterprise license required to keep your data? • Define the rules guidelines how to use the solution Identify minimal solution for use cases

18 Free Excel Template by Vischer Risk assessment template

19 • Before • What is the expected outcome? • How do you want to measure it? • Write it down! • Have a small, but active user group • Not too long – 2-4 weeks might be fine for most cases • Talk to your peple • Measure and decide • Fail is an option! Test with a pilot (or multiple)

20 • Inform well – and often! • Provide training especially for «late majority» • Monitor • Usage • Data sharing • Costs • User feedback • Product lifecycle • Prevent a zoo of tools • Monitor other tools being used • Adapt if necessary Ship to user base

21 Just a few examples… AI tools for daily work

22 • Goal: ai chatbot for knowledge workers • Data base is limited to training data, no «internet search» by default; hallucination & no source provided • Features • Work with texts (marketing, sales, HR, …) • Generate images • Coding: ask for ideas & sample code, fix code, document code, write test cases • Typical users: devs & system enginers, office workers, sales, marketing, C-level, students, your kids • Status: available • Privacy: data stored in US and used for monitoring & training • Pricing: free or 20-30 USD/month for advanced features as GPT4 & image generation • Details OpenAI ChatGPT

23 • Goal: AI pair programmer for engineers • Data: trained on public github repos & user data; beware of weak code and un-licensed code • Features • Generate & refactor code • Inside terminal/console • Chat • Integration (VSC, Neovim, VS, JetBrains) • Typical users: software & system engineers • Status: available • Privacy: data transfered to US (take care of files with secrets), used for training with Individual • Pricing: 10-39 USD/month/user • Details GitHub Copilot

24 • Goal: AI-powered dev environment • Data: trained on public github repos & user data • Features • AI assistant that breaks down issues into plan based on the codebase «plan to code» • Typical users: devs, system engineers • Status: technical preview • Privacy: data transfered to US & ? • Pricing: unknown • Details GitHub Copilot Workspaces Will replace your job! Will replace your job! … handle the ugly tickets!

25 Architecture of MS Copilot https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/ecosystem Data

26 • Goal: ai chatbot powered by Bing • Data: same as GPT4, enriched with Bing search • Features • Ask and search in web – with references • Work with texts and uploaded documents – e.g. summarize long pfs • Generate images • Uses GPT4 / GPT4-turbo • Typical users: everybody • Status: available • Privacy: M365 tenant is boundary, not used for training (with M365 account) • Pricing: free (included in most M365 licenses) • Details Microsoft Copilot with commercial data protection It’s really free! It’s good! Try it!

27 • Goal: your personal AI assistant for office work • Data: graph of your tenant, various MS products and data sources; internet • Features • Teams meetings • Summarize emails and teams chats • Typical users: «office worker» • Status: available • Privacy: M365 tenant is boundary • Pricing: 26.90 CHF/month/user • Details Microsoft Copilot for M365

28 • Goal: ai assistant for Azure • Features • Design: create and configure the services needed while aligning with organizational policies • Operate: answer questions, author complex commands, and manage resources • Troubleshoot: orchestrate across Azure services for insights to summarize issues, identify causes, and suggest solutions • Optimize: improve costs, scalability, and reliability through recommendations for your environment • Typical users: system engineers • Status: limited access • Privacy: Azure tenant is boundary • Pricing: • Details Microsoft Copilot for Azure

29 • Goal: chatbot for security engineers on Azure • Features • Integration into MS security products as Entra, Intune, Defender, Purview and Sentinel • Investigate incidents along tool chain • Generate reports • Typical users: security engineers • Status: available • Privacy: Azure tenant is boundary • Pricing: security compute unit 2’670 CHF/mo • Details Microsoft Copilot for Security

30 Configuration of ready to use AI-tools

31 • Goal: configure chatbots with ai models on your data • Features • Integration with Sharepoint, Jira, Confluence, … • Provide internal support portals – e.g. for IT, HR, • Publish public-facing portals • Of course – integrate with Copilot • Typical users: anybody • Status: available • Privacy: Azure tenant is boundary, specific data protection available • Pricing: 200 CHF / month for 20k messages • Details Microsoft Copilot Studio (aka Power Virtual Agents + OpenAI)

32 Building ai applications with base models, fine-tuning & training

33 • Goal: deploy easy chatbot in your azure tenant • Features • Free blueprints directly deployed in your azure tenant • Provides additional features as document, photo and voice • Typical users: internal ChatGPT similar chatbots • Status: available (blueprints not «prod-ready») • Privacy: Azure tenant is boundary • Pricing: Azure consumption, probably 50-100 CHF / month (depending on usage and data) • Details • Bonus: Azure OpenAI Chatbot with your data in your tenant – in 15min Private Chatbot on Azure OpenAI Blueprints on Azure AI Studio

34 (Probably) related talks today – check the slides OpenAI models with your own data using Azure OpenAI FILIP WOJCIESZYN The Era of Copilots - PoV from Microsoft MIKE BLOECHLINGER RICHARD LAGRANGE Easy On the way to hard Mobi-ChatGPT & Friends - or how to integrate enterprise ready Gen-AI at scale MATTHIAS SCHRANZ ALEXANDER MEIER ChatGPT over your own data MARCO GERBER MICHAEL RÜEFLI Azure AI deep dive with Ausgleichskasse Basel Stadt JÖRG BIERI IVAN BABIC

35 My 5 cent Groth and adjustment of AI market AI will be implemented in every day tools Supporting the users and humans

36 Next steps

The results 37

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And the winners are… (sorry, too late ☺)

Lets discuss

• Next meetup today at 5.45pm @ Puzzle ITC • Uphill Conf Special Meetup – firesidechat with speakers of uphill Applied AI conference • Lisa Carpenter: Lead Data Science & AI Instructor @ Digital Futures • Leandro von Werra: Machine Learning Engineer @ Hugging Face • Pablo Pernías & Dominic Rampas: ML Researchers @ Luma AI, prev. StabilityAI Interested in AI? Join the ML & AI Meetup Bern!

Nexplore AG C.F.L. Lohnerstrasse 24 3645 Gwatt (Thun) Telefon +41 33 334 02 00 [email protected] See you ☺